## 処理設定

verbose = False
check   = False

### 言語の割合の均等化

balanced = True

### LDA 用

## トピック数

n_topics = 15 # 30は多過ぎる？

## doc, term の設定

doc_type      = 'form'  # 変更不可
doc_attr      = 'sound' # spell か sound の選択
max_doc_size  = 12
##
term_size     = 'character'
term_type     = '1gram'

## skippy n-gram の結合範囲

max_distance_val = round(max_doc_size * 0.8)
print(f"max_distance_val: {max_distance_val}")

## ngram を包括的にするかどうか

ngram_is_inclusive = True

### DTM 構築

## term の最低頻度

term_min_freq = 2

## 高頻度 term の濫用指標: 大きくし過ぎないように．0.05 は十分に大きい

term_abuse_threshold = 0.04

max_distance_val: 10

## 言語の選別

##綴り解析で使える言語
## arabic, bengali, chinese, esperanto, english, finnish, french,
## german, greek, hebrew, hungarian, icelandic, irish, italian,
## japanese, russian, spanish, swahili, turkish

##音声解析で使える言語
## arabic, chinese, english, esperanto, french, german, icelandic, japanese, korean, spanish, swahili



import re

select_languages = True
#
spell_set0 = "arabic, bengali, chinese, esperato, english, finnish, french, german, greek, \
                          hebrew, hungarian, icelandic, irish, italian, japanese, \
                          russian, spanish, russian, turkish"
#
spell_set1 = "english, french, german, icelandic, irish, spanish, italian, swahili, turkish"
#
spell_set2 = "arabic, bengali, hebrew, english, french, greek, hungarian, icelandic, russian, spanish, turkish"
#
sound_set0 = "arabic, english, french, german, icelandic, japanese, korean, spanish, swahili"

sound_set1 = "arabic, french, japanese, korean, spanish, swahili"
##
selected_langs = re.split(r",\s*", sound_set1)

print(f"selected languages: {selected_langs}")

selected languages: ['arabic', 'french', 'japanese', 'korean', 'spanish', 'swahili']

import sys, os, random, re, glob
import pandas as pd
import pprint as pp
from functools import reduce

## load data to process
from pathlib import Path
import pprint as pp

wd = Path(".")
dirs = [ x for x in wd.iterdir() if x.is_dir() and not x.match(r"plot*") ]
if verbose:
    print(f"The following {len(dirs)} directories are potential targets:")
    pp.pprint(dirs)

## list up files in target directory 
wd = Path(".")
target_dir = "data-words" # can be changed
target_files = sorted(list(wd.glob(f"{target_dir}/*.csv")))
#
print(f"\n{target_dir} contains {len(target_files)} files to process")
pp.pprint(target_files)

data-words contains 28 files to process
[PosixPath('data-words/base-sound-Arabic-r0-1k-mc.csv'),
 PosixPath('data-words/base-sound-English-r6e-original.csv'),
 PosixPath('data-words/base-sound-French-r0-1k-mc.csv'),
 PosixPath('data-words/base-sound-French-r0-opendic-s900.csv'),
 PosixPath('data-words/base-sound-German-r1a-original.csv'),
 PosixPath('data-words/base-sound-Icelandic-r0-1k-mc.csv'),
 PosixPath('data-words/base-sound-Japanese-r0-1k-mc.csv'),
 PosixPath('data-words/base-sound-Korean-r0-1k-mc-sorted.csv'),
 PosixPath('data-words/base-sound-Spanish-r0-1k-mc.csv'),
 PosixPath('data-words/base-sound-Swahili-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Arabic-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Chinese-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-English-r6e-original.csv'),
 PosixPath('data-words/base-spell-Esperanto-r0-orginal.csv'),
 PosixPath('data-words/base-spell-Finnish-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-French-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-German-r1a-original.csv'),
 PosixPath('data-words/base-spell-Greek-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Hebrew-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Hungarian-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Icelandic-r0-original.csv'),
 PosixPath('data-words/base-spell-Irish-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Italian-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Japanese-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Russian-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Spanish-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Swahili-r0-1k-mc.csv'),
 PosixPath('data-words/base-spell-Turkish-r0-1k-mc.csv')]

import pandas as pd

## データ型の辞書
types = re.split(r",\s+", "spell, sound")
type_settings = { t : 0 for t in types }
print(type_settings)

## 言語名の辞書
lang_name_list = "arabic, bengali, chinese, english, esperanto, \
    finnish, french, greek, galician, german, hebrew, hungarian, \
    icelandic, irish, italian, japanese, korean, russian, spanish, swahili, turkish"

langs = re.split(r",\s*", lang_name_list)
lang_settings = { lang : 0 for lang in langs }
print(f"{len(lang_settings.keys())} langs are available")
print(lang_settings)

## 辞書と統合
settings = { **type_settings, **lang_settings }
print(settings)

{'spell': 0, 'sound': 0}
21 langs are available
{'arabic': 0, 'bengali': 0, 'chinese': 0, 'english': 0, 'esperanto': 0, 'finnish': 0, 'french': 0, 'greek': 0, 'galician': 0, 'german': 0, 'hebrew': 0, 'hungarian': 0, 'icelandic': 0, 'irish': 0, 'italian': 0, 'japanese': 0, 'korean': 0, 'russian': 0, 'spanish': 0, 'swahili': 0, 'turkish': 0}
{'spell': 0, 'sound': 0, 'arabic': 0, 'bengali': 0, 'chinese': 0, 'english': 0, 'esperanto': 0, 'finnish': 0, 'french': 0, 'greek': 0, 'galician': 0, 'german': 0, 'hebrew': 0, 'hungarian': 0, 'icelandic': 0, 'irish': 0, 'italian': 0, 'japanese': 0, 'korean': 0, 'russian': 0, 'spanish': 0, 'swahili': 0, 'turkish': 0}

check = False

setting_keys = list(settings.keys())
print(f"target setting_keys: {setting_keys}")
d_parts = [ ]
for lang in lang_settings.keys():
    local_settings = settings.copy()
    if check:
        print(f"processing: {lang}")
    try:
        ## ファイル単位で処理
        for f in [ f for f in target_files if lang.capitalize() in str(f) ]:
            print(f"reading: {f}")
            
            # 言語名の指定
            local_settings[lang] = 1
            
            # 型名の指定
            for key in type_settings.keys():
                if key in str(f):
                    local_settings[key] = 1
                else:
                    local_settings[key] = 0 # この変更を見落とさないように
            
            # ファイル処理
            try:
                data = pd.read_csv(f, encoding = 'utf-8', sep = ",", on_bad_lines = 'skip') # Crucially, ...= skip
                if check:
                    print(data)
                #dfx = pd.DataFrame(data, columns = setting_keys)
                dfx = pd.DataFrame(data, columns = ['form', 'freq'])
                for key in settings.keys():
                    dfx[key] = local_settings[key]
                    if check:
                        print(dfx)
                d_parts.append(dfx)
            except FileNotFoundError:
                pass
    except IndexError:
        pass
#
if verbose:
    d_parts

target setting_keys: ['spell', 'sound', 'arabic', 'bengali', 'chinese', 'english', 'esperanto', 'finnish', 'french', 'greek', 'galician', 'german', 'hebrew', 'hungarian', 'icelandic', 'irish', 'italian', 'japanese', 'korean', 'russian', 'spanish', 'swahili', 'turkish']
reading: data-words/base-sound-Arabic-r0-1k-mc.csv
reading: data-words/base-spell-Arabic-r0-1k-mc.csv
reading: data-words/base-spell-Chinese-r0-1k-mc.csv
reading: data-words/base-sound-English-r6e-original.csv
reading: data-words/base-spell-English-r6e-original.csv
reading: data-words/base-spell-Esperanto-r0-orginal.csv
reading: data-words/base-spell-Finnish-r0-1k-mc.csv
reading: data-words/base-sound-French-r0-1k-mc.csv
reading: data-words/base-sound-French-r0-opendic-s900.csv
reading: data-words/base-spell-French-r0-1k-mc.csv
reading: data-words/base-spell-Greek-r0-1k-mc.csv
reading: data-words/base-sound-German-r1a-original.csv
reading: data-words/base-spell-German-r1a-original.csv
reading: data-words/base-spell-Hebrew-r0-1k-mc.csv
reading: data-words/base-spell-Hungarian-r0-1k-mc.csv
reading: data-words/base-sound-Icelandic-r0-1k-mc.csv
reading: data-words/base-spell-Icelandic-r0-original.csv
reading: data-words/base-spell-Irish-r0-1k-mc.csv
reading: data-words/base-spell-Italian-r0-1k-mc.csv
reading: data-words/base-sound-Japanese-r0-1k-mc.csv
reading: data-words/base-spell-Japanese-r0-1k-mc.csv
reading: data-words/base-sound-Korean-r0-1k-mc-sorted.csv
reading: data-words/base-spell-Russian-r0-1k-mc.csv
reading: data-words/base-sound-Spanish-r0-1k-mc.csv
reading: data-words/base-spell-Spanish-r0-1k-mc.csv
reading: data-words/base-sound-Swahili-r0-1k-mc.csv
reading: data-words/base-spell-Swahili-r0-1k-mc.csv
reading: data-words/base-spell-Turkish-r0-1k-mc.csv

## データ統合

raw_df = pd.concat(d_parts)
raw_df

## 言語名= language の列を追加

check = False
language_vals = [ ]
for i, row in raw_df.iterrows():
    if check:
        print(row)
    for j, lang in enumerate(langs):
        if check:
            print(f"{i}: {lang}")
        if row[lang] == 1:
            language_vals.append(lang)
if verbose:
    print(language_vals)
    len(language_vals)
#
raw_df['language'] = language_vals
raw_df

## 文字数の列を追加

raw_df['size'] = [ len(x) for x in raw_df[doc_type] ]
raw_df

## 解析データの型指定

print(f"doc_attr: {doc_attr}")
raw_df = raw_df[raw_df[doc_attr] == 1]
raw_df

doc_attr: sound

## 言語の選別

if select_languages:
    df_new = [ ]
    for lang in selected_langs:
        df_new.append(raw_df[raw_df[lang] == 1])
    raw_df = pd.concat(df_new)
#
raw_df

## 文字数の分布
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(1,1,1)

ax.hist(raw_df['size'], bins = 40)
ax.set_xlabel('length of doc')
ax.set_ylabel('freq')
plt.title(f"Length distribution for docs")
fig.show()

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_4782/1088473461.py:12: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
  fig.show()

## 長さで濾過

print(f"max doc size: {max_doc_size}")
original_size = len(raw_df)
raw_df = raw_df[raw_df['size'] < max_doc_size]
filtered_size = len(raw_df)
print(f"{original_size - filtered_size} cases removed")

max doc size: 12
116 cases removed

## 結果の検査 1

for type in types:
    print(raw_df[type].value_counts())

spell
0    5052
Name: count, dtype: int64
sound
1    5052
Name: count, dtype: int64

## 結果の検査 2

for lang in langs:
    print(raw_df[lang].value_counts())

arabic
0    4328
1     724
Name: count, dtype: int64
bengali
0    5052
Name: count, dtype: int64
chinese
0    5052
Name: count, dtype: int64
english
0    5052
Name: count, dtype: int64
esperanto
0    5052
Name: count, dtype: int64
finnish
0    5052
Name: count, dtype: int64
french
0    4093
1     959
Name: count, dtype: int64
greek
0    5052
Name: count, dtype: int64
galician
0    5052
Name: count, dtype: int64
german
0    5052
Name: count, dtype: int64
hebrew
0    5052
Name: count, dtype: int64
hungarian
0    5052
Name: count, dtype: int64
icelandic
0    5052
Name: count, dtype: int64
irish
0    5052
Name: count, dtype: int64
italian
0    5052
Name: count, dtype: int64
japanese
0    4234
1     818
Name: count, dtype: int64
korean
0    4331
1     721
Name: count, dtype: int64
russian
0    5052
Name: count, dtype: int64
spanish
0    4103
1     949
Name: count, dtype: int64
swahili
0    4171
1     881
Name: count, dtype: int64
turkish
0    5052
Name: count, dtype: int64

## 統合: 割合補正を適用

eng_reduct_factor = 0.2
if balanced:
    eng_df = raw_df[raw_df['english'] == 1]
    non_eng_df = raw_df[raw_df['english'] == 0]
    eng_reduced_df = eng_df.sample(round(len(eng_df) * eng_reduct_factor))
    raw_df = pd.concat([eng_reduced_df, non_eng_df])
raw_df

## 結果の検査 3

for lang in langs:
    print(raw_df[lang].value_counts())

arabic
0    4328
1     724
Name: count, dtype: int64
bengali
0    5052
Name: count, dtype: int64
chinese
0    5052
Name: count, dtype: int64
english
0    5052
Name: count, dtype: int64
esperanto
0    5052
Name: count, dtype: int64
finnish
0    5052
Name: count, dtype: int64
french
0    4093
1     959
Name: count, dtype: int64
greek
0    5052
Name: count, dtype: int64
galician
0    5052
Name: count, dtype: int64
german
0    5052
Name: count, dtype: int64
hebrew
0    5052
Name: count, dtype: int64
hungarian
0    5052
Name: count, dtype: int64
icelandic
0    5052
Name: count, dtype: int64
irish
0    5052
Name: count, dtype: int64
italian
0    5052
Name: count, dtype: int64
japanese
0    4234
1     818
Name: count, dtype: int64
korean
0    4331
1     721
Name: count, dtype: int64
russian
0    5052
Name: count, dtype: int64
spanish
0    4103
1     949
Name: count, dtype: int64
swahili
0    4171
1     881
Name: count, dtype: int64
turkish
0    5052
Name: count, dtype: int64

## 順序のランダマイズし，基本データを決定

import sklearn.utils
df = sklearn.utils.shuffle(raw_df)

## ngram の追加

import sys
sys.path.append('..')

import re
import ngrams
import importlib
importlib.reload(ngrams)
import ngrams_skippy

bases = df[doc_type]

## 1gram 列の追加
#sep = r""
#unigrams = [ list(filter(lambda x: len(x) > 0, y)) for y in [ re.split(sep, z) for z in bases ] ]
unigrams = ngrams.gen_unigrams(bases, sep = r"", check = False)
if verbose:
    random.sample(unigrams, 5)
#
df['1gram'] = unigrams
df

## 2gram列の追加

bigrams = ngrams.gen_bigrams(bases, sep = r"", check = False)

## 包括的 2gram の作成
if ngram_is_inclusive:
    bigrams = [ [*b, *u] for b, u in zip(bigrams, unigrams) ]

if verbose:
    print(random.sample(bigrams, 3))

df['2gram'] = bigrams
if verbose:
    df

## 3gram列の追加

trigrams = ngrams.gen_trigrams(bases, sep = r"", check = False)

## 包括的 3gram の作成
if ngram_is_inclusive:
    trigrams = [ [ *t, *b ] for t, b in  zip(trigrams, bigrams) ]

if verbose:
    print(random.sample(trigrams, 3))

df['3gram'] = trigrams
if verbose:
    df

## skippy 2grams の生成

import sys
sys.path.append("..") # library path に一つ上の階層を追加

import ngrams_skippy

skippy_2grams = [ ngrams_skippy.generate_skippy_bigrams(x,
                                                         missing_mark = '…',
                                                         max_distance = max_distance_val, check = False)
                  for x in df['1gram'] ]

## 包括的 skippy 2-grams の生成
if ngram_is_inclusive:
    for i, b2 in enumerate(skippy_2grams):
        b2.extend(unigrams[i])
#
if verbose:
    random.sample(skippy_2grams, 3)

## skippy 2gram 列の追加

df['skippy2gram'] = skippy_2grams
df

## skippy 3grams の生成

#import sys
#sys.path.append("..") # library path に一つ上の階層を追加

import ngrams_skippy

skippy_3grams = [ ngrams_skippy.generate_skippy_trigrams(x,
                                                         missing_mark = '…',
                                                         max_distance = max_distance_val, check = False)
                  for x in df['1gram'] ]

## 包括的 skippy 3-grams の生成
if ngram_is_inclusive:
    for i, t2 in enumerate(skippy_3grams):
        t2.extend(skippy_2grams[i])
#
if verbose:
    random.sample(skippy_3grams, 3)

## skippy 3gram 列の追加

df['skippy3gram'] = skippy_3grams
df

## LDA 構築の基になる document-term matrix (dtm) を構築

from gensim.corpora.dictionary import Dictionary

bots = df[term_type]
diction = Dictionary(bots)

## 結果の確認
print(diction)

Dictionary<85 unique tokens: ['a', 'd', 'r', 'ʔ', 'ˤ']...>

## diction の濾過

import copy
diction_copy = copy.deepcopy(diction)

## filter適用: 実は諸刃の刃で，token数が少ない時には適用しない方が良い
print(f"min freq filter: {term_min_freq}")
print(f"abuse filter: {term_abuse_threshold}")

apply_filter = True
if apply_filter:
    diction_copy.filter_extremes(no_below = term_min_freq, no_above = term_abuse_threshold)

## check
print(diction_copy)

min freq filter: 2
abuse filter: 0.04
Dictionary<53 unique tokens: ['ʔ', 'ˤ', 'ʕ', 'ˈ', 'ə']...>

## Corpus (gensim の用語では corpus) の構築

corpus = [ diction.doc2bow(bot) for bot in bots ]

## check
check = True
if verbose:
    sample_n = 5
    print(random.sample(corpus, sample_n))
#
print(f"Number of documents: {len(corpus)}")

Number of documents: 5052

## LDA モデルの構築

from gensim.models import LdaModel
#from tqdm import tqdm

## LDAモデル
print(f"Building LDA model with n_topics: {n_topics}")
lda = LdaModel(corpus, id2word = diction, num_topics = n_topics, alpha = 0.01)
#
print(lda) # print(..)しないと中身が見れない

Building LDA model with n_topics: 15
LdaModel<num_terms=85, num_topics=15, decay=0.5, chunksize=2000>

%%capture --no-display

## LDA のtopic ごとに，関連度の高い term を表示

import pandas as pd

n_terms = 40 # topic ごとに表示する term 数の指定
topic_dfs = [ ]
for topic in range(n_topics):
    terms = [ ]
    for i, prob in lda.get_topic_terms(topic, topn = n_terms):
        terms.append(diction.id2token[ int(i) ])
    #
    topic_dfs.append(pd.DataFrame([terms], index = [ f'topic {topic+1}' ]))
#
topic_term_df = pd.concat(topic_dfs)

## Table で表示
topic_term_df.T

%%capture --no-display

## pyLDAvis を使った結果 LDA の可視化: 階層クラスタリングより詳しい

import pyLDAvis

#installed_version = sys.version
installed_version = pyLDAvis.__version__

print(f"installed_version: {installed_version}")

if float(installed_version[:3]) > 3.1:
    import pyLDAvis.gensim_models as gensimvis
else:
    import pyLDAvis.gensim as gensimvis
#
pyLDAvis.enable_notebook()

#
lda_used     = lda
corpus_used  = corpus
diction_used = diction

## 実行パラメター
use_tSNE = False
if use_tSNE:
    vis = gensimvis.prepare(lda_used, corpus_used, diction_used, mds = 'tsne',
                            n_jobs = 1, sort_topics = False)
else:
    vis = gensimvis.prepare(lda_used, corpus_used, diction_used,
                            n_jobs = 1, sort_topics = False)
#
pyLDAvis.display(vis)
## topic を表わす円の重なりが多いならn_topics が多過ぎる可能性がある．
## ただし2Dで重なっていても，3Dなら重なっていない可能性もある

## LDA がD に対して生成した topics の弁別性を確認

## 得られたtopics を確認
topic_dist = lda.get_topics()
if verbose:
    topic_dist

## 検査 1: topic ごとに分布の和を取る

print(topic_dist.sum(axis = 1))

[0.9999999  0.99999994 1.0000002  1.         0.9999998  1.
 0.99999994 0.99999994 0.99999994 0.9999999  1.         0.99999994
 1.         1.0000001  0.99999994]

## 検査 2: 総和を求める: n_topics にほぼ等しいなら正常

print(topic_dist.sum())

15.0

## term エンコード値の分布を確認

import matplotlib.pyplot as plt

plt.figure(figsize = (4,5))

sampling_rate = 0.3
df_size = len(topic_dist)
sample_n = round(df_size * sampling_rate)
topic_sampled = random.sample(list(topic_dist), sample_n)

T = sorted([ sorted(x, reverse = True) for x in topic_sampled ])

plt.plot(T, range(len(T)))
plt.title(f"Distribution of sorted values ({sample_n} samples) for topic/term encoding")
plt.show()

## tSNE を使った topics のグループ化 (3D)

from sklearn.manifold import TSNE
import numpy as np

## tSNE のパラメターを設定
## n_components は射影先の空間の次元: n_components = 3 なら3次元空間に射影
## perplexity は結合の強さを表わす指数で，値に拠って結果が代わるので，色々な値を試すと良い

#perplexity_val = 10 # 大き過ぎると良くない
top_perplexity_reduct_rate = 0.3
perplexity_val = round(len(topic_dist) * top_perplexity_reduct_rate)
topic_tSNE_3d = TSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)

## データに適用
top_tSNE_3d_fitted = topic_tSNE_3d.fit_transform(np.array(topic_dist))

## Plotlyを使って tSNE の結果の可視化 (3D)

#import plotly.express as pex
import plotly.graph_objects as go
import numpy as np

top_tSNE = top_tSNE_3d_fitted
fig = go.Figure(data = [go.Scatter3d(x = top_tSNE[:,0], y = top_tSNE[:,1], z = top_tSNE[:,2],
                                     mode = 'markers')])

## 3D 散布図にラベルを追加する処理は未実装
title_val = f"3D tSNE view for LDA (#topics: {n_topics}, doc: {doc_type}, term: {term_type})"
fig.update_layout(autosize = False,
                  width = 600, height = 600, title = title_val)
fig.show()

## 構築した LDA モデルを使って文(書)を分類する

## .get_document_topics(..) は　minimu_probability = 0としないと
## topic の値が小さい場合に値を返さないので，

## パラメター

ntopics = n_topics # LDA の構築の最に指定した値を使う

check      = False
encoding = [ ]
for i, row in df.iterrows():
    if check:
        print(f"row: {row}")
    doc = row[doc_type]
    bot = row[term_type]
    ## get_document_topics(..) では　minimu_probability = 0 としないと
    ## 値が十分に大きな topics に関してだけ値が取れる
    enc = lda.get_document_topics(diction.doc2bow(bot), minimum_probability = 0)
    if check:
        print(f"enc: {enc}")
    encoding.append(enc)
#
len(encoding)

5052

## enc 列の追加

#df['enc'] = np.array(encoding) # This flattens arrays
#df['enc'] = list(encoding) # ineffective
df['enc'] = [ list(map(lambda x: x[1], y)) for y in encoding ]
if verbose:
    df['enc']

## エンコーディングのstd の分布を見る

from scipy.stats import tstd
from matplotlib import pyplot as plt

plt.figure(figsize = (6,4))

std_data = [ tstd(x) for x in df['enc'] ]
plt.hist(std_data)

plt.title("Distribution of standard deviations")
plt.show()

## doc のエンコーディング

## 一様分布の事例を除外

from scipy.stats import tstd # standard deviation の計算用

print(f"{len(df)} instances before filtering")

check = False

doc_enc = df['enc']
max_std = max([ tstd(x) for x in doc_enc])
if check: print(f"std max: {max_std}")

min_std = min([ tstd(x) for x in doc_enc])
if check: print(f"std min: {min_std}")

first_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-0]
print(f"std 1st min: {first_min_std}")

second_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-1]
print(f"std 2nd min: {second_min_std}")

5052 instances before filtering
std 1st min: 0.1351495166475591
std 2nd min: 0.25472535598622603

## df_filtered の定義

## 閾値は2番目に小さい値より小さく最小値よりは大きな値であるべき
std_threshold = second_min_std / 4 # 穏健な値を得るために4で割った
print(f"std_threshold: {std_threshold}")

## Rっぽい次のコードは通らない
#df_filtered = df[ df['encoding'] > std_threshold ]

## 通るのは次のコード: Creating a list of True/False and apply it to DataFrame 
std_tested = [ False if tstd(x) < std_threshold else True for x in df['enc'] ]
df_filtered = df[ std_tested ]
#
print(f"{len(df_filtered)} instances after filtering ({len(df) - len(df_filtered)} instances removed)")

std_threshold: 0.06368133899655651
5052 instances after filtering (0 instances removed)

## doc エンコード値の分布を確認

sample_n = 50
E = sorted([ sorted(x, reverse = True) for x in df_filtered['enc'].sample(sample_n) ])

plt.figure(figsize = (5,5))
plt.plot(E, range(len(E)))
plt.title(f"Distribution of sorted encoding values for sampled {sample_n} docs")
plt.show()

len(df_filtered['language'])

5052

df_filtered['language'].value_counts

<bound method IndexOpsMixin.value_counts of 355     arabic
70      arabic
53     spanish
381    swahili
434     french
        ...   
432    spanish
849    spanish
380     arabic
665     arabic
562    swahili
Name: language, Length: 5052, dtype: object>

## tSNE 用の事例サンプリング = tSNE_df の定義

tSNE_sampling = True
tSNE_sampling_rate = 0.33
if tSNE_sampling:
    tSNE_df_original = df_filtered.copy()
    sample_n = round(len(tSNE_df_original) * tSNE_sampling_rate)
    tSNE_df = tSNE_df_original.sample(sample_n)
    print(f"tSNE_df has {len(tSNE_df)} rows after sampling")
else:
    tSNE_df = df_filtered

tSNE_df has 1667 rows after sampling

tSNE_df.columns

Index(['form', 'freq', 'spell', 'sound', 'arabic', 'bengali', 'chinese',
       'english', 'esperanto', 'finnish', 'french', 'greek', 'galician',
       'german', 'hebrew', 'hungarian', 'icelandic', 'irish', 'italian',
       'japanese', 'korean', 'russian', 'spanish', 'swahili', 'turkish',
       'language', 'size', '1gram', '2gram', '3gram', 'skippy2gram',
       'skippy3gram', 'enc'],
      dtype='object')

tSNE_df['language'].value_counts

<bound method IndexOpsMixin.value_counts of 237     spanish
442     swahili
816     spanish
228     spanish
391    japanese
         ...   
645      korean
357      korean
164      korean
110     swahili
57      spanish
Name: language, Length: 1667, dtype: object>

## tSNE の結果の可視化: Plotly を使った 3D 描画

import numpy as np
from sklearn.manifold import TSNE as tSNE

import plotly.express as pex
import plotly.graph_objects as go
import matplotlib.pyplot as plt

## tSNE のパラメターを設定

perplexity_max_val = round(len(tSNE_df)/4)

for perplexity_val in range(5, perplexity_max_val, 60):
    
    ## tSNE 事例の生成
    tSNE_3d_varied = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)

    ## データに適用
    doc_enc = np.array(list(tSNE_df['enc']))
    doc_tSNE_3d_varied = tSNE_3d_varied.fit_transform(doc_enc)

    T = zip(doc_tSNE_3d_varied[:,0], doc_tSNE_3d_varied[:,1], doc_tSNE_3d_varied[:,2],
            tSNE_df['language']) # zip(..)が必要
    df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])

    ## 作図
    fig = go.Figure()
    for lang in np.unique(df['language']):
        part = df[df['language'] == lang]
        fig.add_trace(
            go.Scatter3d(
                x = part['D1'], y = part['D2'], z = part['D3'],
                name = lang, mode = 'markers', marker = dict(size = 4),
                showlegend = True
            )
        )

    title_val = f"tSNE 3D map (ppl: {perplexity_val}) of '{doc_attr}'s encoded\n by LDA ({n_topics} topics, {term_type})"
    fig.update_layout(title = dict(text = title_val),
                      autosize = False, width = 600, height = 600,)
    fig.show()

## 階層クラスタリングのための事例のサンプリング

hc_sampling_rate = 0.1 # 大きくし過ぎると図が見にくい
df_size = len(tSNE_df)
hc_sample_n = round(df_size * hc_sampling_rate)
hc_df = tSNE_df.sample(hc_sample_n)
##
print(f"{hc_sample_n} rows are sampled")
hc_df['language'].value_counts()

167 rows are sampled

language
french      32
arabic      30
spanish     30
japanese    28
korean      26
swahili     21
Name: count, dtype: int64

## 日本語表示のための設定
#import matplotlib.pyplot as plt
#plt.rcParams["font.family"] = "Hiragino Sans" # Windows は別のフォント名を指定する必要がある
#plt.rcParams["font.family"] = "Lucida Sans Unicode"

## doc 階層クラスタリングの実行

import numpy as np
import plotly
import matplotlib.pyplot as plt
## 次の設定は arabic が文字化けする
#plt.rcParams["font.family"] = "Hiragino Sans" # Windows は別のフォント名を指定する必要がある
#plt.rcParams["font.family"] = "Lucida Sans Unicode"

from scipy.cluster.hierarchy import dendrogram, linkage

## 距離行列の生成
Enc = list(hc_df['enc'])
linkage = linkage(Enc, method = 'ward', metric = 'euclidean')

## 描画サイズの指定
plt.figure(figsize = (5, round(len(hc_df) * 0.15))) # This needs to be run here, before dendrogram construction.

## 事例ラベルの生成
label_vals = [ x[:max_doc_size] for x in list(hc_df[doc_type]) ] # truncate doc keys

## 樹状分岐図の作成
dendrogram(linkage, orientation = 'left', labels = label_vals, leaf_font_size = 7)

## 描画
plt.title(f"Hierarchical clustering of (sampled) {len(hc_df)} (= {100 * hc_sampling_rate}%) {doc_attr}s as docs\n \
    encoded via LDA ({n_topics} topics) with {term_type} as terms")

## ラベルに language に対応する色を付ける
lang_colors = { lang_name : i for i, lang_name in enumerate(np.unique(hc_df['language'])) }
ax = plt.gca()
for ticker in ax.get_ymajorticklabels():
    form = ticker.get_text()
    row = hc_df.loc[hc_df[doc_type] == form]
    #lang = row['language']
    lang = row['language'].to_string().split()[-1] # trick
    try:
        lang_id = lang_colors[lang]
    except (TypeError, KeyError):
        print(f"color encoding error at: {lang}")
    #
    ticker.set_color(plotly.colors.qualitative.Plotly[lang_id]) # id の基数調整
#
plt.show()

## tSNE の結果の可視化 (2D)

#import seaborn as sns
import numpy as np
import plotly
import plotly.express as pex
import matplotlib.pyplot as plt
from adjustText import adjust_text

## tSNE 事例の生成
perplexity_selected = 150
tSNE_3d = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_selected, n_iter = 1000)

## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d = tSNE_3d.fit_transform(doc_enc)

T = zip(doc_tSNE_3d[:,0], doc_tSNE_3d[:,1], doc_tSNE_3d[:,2],
        tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])

## 描画
## 次の設定は arabic が文字化けする
#plt.rcParams["font.family"] = "Hiragino Sans" # Windows は別のフォント名を指定する必要がある
#plt.rcParams["font.family"] = "Lucida Sans Unicode"
plt.figure(figsize = (5, 5))
plt.set_colors = pex.colors.qualitative.Plotly
    
for roll in [ np.roll([0,1,2], -i) for i in range(0,3) ]:
    if check:
        print(r)
    X, Y = df.iloc[:,roll[0]], df.iloc[:,roll[1]]
    gmax = max(X.max(), Y.max())
    gmin = min(X.min(), Y.min())
    plt.xlim(gmin, gmax)
    plt.ylim(gmin, gmax)

    colormap = pex.colors.qualitative.Plotly
    lang_list = list(np.unique(tSNE_df['language']))
    cmapped = [ colormap[lang_list.index(lang)] for lang in df['language'] ]
    scatter = plt.scatter(X, Y, s = 40, c = cmapped, edgecolors = 'w')

    ## 文字を表示する事例のサンプリング
    lab_sampling_rate = 0.02
    lab_sample_n = round(len(tSNE_df) * lab_sampling_rate)
    sampled_keys = [ doc[:max_doc_size] for doc in random.sample(list(tSNE_df[doc_type]), lab_sample_n) ]

    ## labels の生成
    texts = [ ]
    for x, y, s in zip(X, Y, sampled_keys):
        texts.append(plt.text(x, y, s, size = 9, color = 'blue'))

    ## label に repel を追加: adjustText package の導入が必要
    adjust_text(texts, force_points = 0.2, force_text = 0.2,
                expand_points = (1, 1), expand_text = (1, 1),
                arrowprops = dict(arrowstyle = "-", color = 'black', lw = 0.5))
    #

    plt.title(f"tSNE (ppl: {perplexity_selected}) 2D map of {len(tSNE_df)} {doc_attr}s via LDA ({term_type}; {n_topics} topics)")
    #plt.legend(cmapped)
    plt.legend(df['language'])
    plt.show()

	topic 1	topic 2	topic 3	topic 4	topic 5	topic 6	topic 7	topic 8	topic 9	topic 10	topic 11	topic 12	topic 13	topic 14	topic 15
0	a	t	i	ʌ	ʁ	ɯ	e	a	a	i	u	o	a	i	a
1	ː	a	a	̹	a	o	i	e	̠	ɕ	s	e	s	a	k
2	i	e	k	k	ɔ	i	p	ɾ	k	t	j	ɾ	o	n	o
3	m	i	s	a	̃	m	o	j	n	ʰ	i	ɯ	t	k	u
4	l	n	o	j	s	s	a	m	e	͡	o	k	b	l	i
5	d	s	ɾ	t	t	ɾ	l	o	s	̞	n	t	l	z	t
6	r	ʁ	u	̚	ɛ	e	u	t	ɯ	d	θ	d	i	t	m
7	u	m	x	i	ə	t	ɾ	ð	ɭ	o	a	ː	ː	m	ɗ
8	t	ɛ	m	n	i	ɕ	ɓ	p	m	ʑ	̝	i	u	p	g
9	s	k	e	o	l	n	w	s	u	a	ɛ	b	e	ɾ	n
10	n	d	p	̠	k	k	s	k	o	̠	d	a	ɯ	w	ᵑ
11	ħ	o	ɯ	l	e	β	k	n	i	ŋ	k	n	k	e	w
12	k	ɾ	t	ŋ	j	ɣ	z	ˈ	ʰ	ɴ	h	s	ʰ	s	s
13	ʕ	b	ç	p	ɑ	ɴ	t	l	ɾ	u	ɯ	p	ˤ	ɔ	z
14	f	ʃ	ɴ	u	p	ʎ	ᵐ	i	ŋ	m	f	ɸ	n	ʃ	ⁿ
15	ˤ	̃	l	m	y	ɰ	f	g	j	e	ŋ	m	̠	d	ʄ
16	ʔ	u	ː	ɭ	d	g	θ	u	p	ɯ	e	ʃ	̞	ɣ	ʃ
17	q	z	h	ʰ	b	p	v	r	t	p	ɾ	θ	ɾ	o	ː
18	h	j	n	̞	f	b	j	d	x	n	l	l	ɴ	j	l
19	ʒ	ɑ	ˈ	͡	v	ː	ɗ	b	ɛ	ɡ	ʰ	̃	ˈ	u	ɠ
20	b	l	g	͈	g	x	ⁿ	β	θ	ː	p	g	ɡ	̠	r
21	w	ɔ	f	s	m	w	n	ʁ	w	h	ˈ	ɑ	m	ɡ	ɯ
22	ɓ	ə	ð	ɯ	ʒ	z	g	w	b	j	ɦ	ʑ	x	ɯ	d
23	ɾ	ː	j	ɕ	z	u	ɪ	f	d	b	m	β	ʔ	ʁ	p
24	ʃ	ɥ	ɕ	d	n	ɭ	ð	ʷ	ˈ	̚	t	z	ᵝ	v	b
25	ʊ	̞	ᵑ	ɾ	u	d	d	ɯ	̚	̹	ː	ɔ	ɰ	ɴ	e
26	ð	p	ɔ	b	ʃ	ʁ	ɲ	ɲ	l	ʌ	̹	ð	d	ʰ	f
27	z	̠	ˤ	ɡ	w	͡	r	y	̝	ɾ	ʌ	ɲ	p	y	ɣ
28	j	̝	ʁ	ʑ	œ	ʃ	ɠ	̠	ː	ɦ	̠	ɛ	β	̃	ɾ
29	g	͡	ʰ	e	ɲ	r	ᵑ	ː	β	k	ɓ	ɕ	f	ɕ	ɲ
30	ᵐ	ʎ	r	ː	ᵝ	l	ɛ	ŋ	ɡ	ɛ	ɡ	f	ɕ	ø	x
31	ʄ	v	ᵝ	ʝ	ɰ	̃	ʄ	͈	r	ɓ	ʑ	ə	w	ʑ	q
32	θ	ɕ	d	w	ʼ	ð	̞	ʎ	ʁ	̝	b	ʄ	ɛ	͈	h
33	ɗ	w	ʃ	ɲ	̠	y	ɴ	̃	͈	s	ɔ	ʷ	j	ʒ	ˤ
34	ɯ	͈	y	ɦ	ː	ɛ	ɣ	̞	̞	ᵐ	ɴ	ɴ	ʁ	ː	ʔ
35	ᵑ	ɯ	z	h	ø	ɑ	β	ɛ	̹	ʃ	r	x	̚	θ	ʒ
36	̠	g	̠	ʔ	h	ɔ	̚	ɔ	ʌ	̃	ɗ	ˈ	g	ɲ	j
37	e	ʒ	w	ɗ	o	v	b	ɕ	ð	ɔ	g	j	ŋ	͡	ɴ
38	p	ɲ	b	ɛ	ˤ	̞	ʒ	ᵑ	z	l	ʄ	ᵝ	ʃ	g	ʕ
39	o	̚	ɗ	q	ɾ	h	ɔ	θ	ɔ	ʁ	w	ɰ	ʕ	ɑ	v

	form	freq	spell	sound	arabic	bengali	chinese	english	esperanto	finnish	...	hungarian	icelandic	irish	italian	japanese	korean	russian	spanish	swahili	turkish
0	kamaː	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	ʔanaː	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	lahu	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	ʔan	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	huːa	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
994	burun	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
995	çoğul	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
996	öfke	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
997	iddia	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
998	kıta	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1

	form	freq	spell	sound	arabic	bengali	chinese	english	esperanto	finnish	...	icelandic	irish	italian	japanese	korean	russian	spanish	swahili	turkish	language
0	kamaː	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	arabic
1	ʔanaː	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	arabic
2	lahu	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	arabic
3	ʔan	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	arabic
4	huːa	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	arabic
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
994	burun	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	turkish
995	çoğul	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	turkish
996	öfke	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	turkish
997	iddia	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	turkish
998	kıta	1	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	turkish

	form	freq	spell	sound	arabic	bengali	chinese	english	esperanto	finnish	...	italian	japanese	korean	russian	spanish	swahili	turkish	language	size	1gram
355	ʔarradˤa	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	arabic	8	[ʔ, a, r, r, a, d, ˤ, a]
70	faʕala	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	arabic	6	[f, a, ʕ, a, l, a]
53	aˈsi	1.0	0	1	0	0	0	0	0	0	...	0	0	0	0	1	0	0	spanish	4	[a, ˈ, s, i]
381	alisimama	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	1	0	swahili	9	[a, l, i, s, i, m, a, m, a]
434	amɔli	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	french	5	[a, m, ɔ, l, i]
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
432	mexoɾ	1.0	0	1	0	0	0	0	0	0	...	0	0	0	0	1	0	0	spanish	5	[m, e, x, o, ɾ]
849	diskuɾso	1.0	0	1	0	0	0	0	0	0	...	0	0	0	0	1	0	0	spanish	8	[d, i, s, k, u, ɾ, s, o]
380	baːrid	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	arabic	6	[b, a, ː, r, i, d]
665	aːlnasaːʔ	1	0	1	1	0	0	0	0	0	...	0	0	0	0	0	0	0	arabic	9	[a, ː, l, n, a, s, a, ː, ʔ]
562	ɗamu	1	0	1	0	0	0	0	0	0	...	0	0	0	0	0	1	0	swahili	4	[ɗ, a, m, u]

	topic 1	topic 2	topic 3	topic 4	topic 5	topic 6	topic 7	topic 8	topic 9	topic 10	topic 11	topic 12	topic 13	topic 14	topic 15
0	a	t	i	ʌ	ʁ	ɯ	e	a	a	i	u	o	a	i	a
1	ː	a	a	̹	a	o	i	e	̠	ɕ	s	e	s	a	k
2	i	e	k	k	ɔ	i	p	ɾ	k	t	j	ɾ	o	n	o
3	m	i	s	a	̃	m	o	j	n	ʰ	i	ɯ	t	k	u
4	l	n	o	j	s	s	a	m	e	͡	o	k	b	l	i
5	d	s	ɾ	t	t	ɾ	l	o	s	̞	n	t	l	z	t
6	r	ʁ	u	̚	ɛ	e	u	t	ɯ	d	θ	d	i	t	m
7	u	m	x	i	ə	t	ɾ	ð	ɭ	o	a	ː	ː	m	ɗ
8	t	ɛ	m	n	i	ɕ	ɓ	p	m	ʑ	̝	i	u	p	g
9	s	k	e	o	l	n	w	s	u	a	ɛ	b	e	ɾ	n
10	n	d	p	̠	k	k	s	k	o	̠	d	a	ɯ	w	ᵑ
11	ħ	o	ɯ	l	e	β	k	n	i	ŋ	k	n	k	e	w
12	k	ɾ	t	ŋ	j	ɣ	z	ˈ	ʰ	ɴ	h	s	ʰ	s	s
13	ʕ	b	ç	p	ɑ	ɴ	t	l	ɾ	u	ɯ	p	ˤ	ɔ	z
14	f	ʃ	ɴ	u	p	ʎ	ᵐ	i	ŋ	m	f	ɸ	n	ʃ	ⁿ
15	ˤ	̃	l	m	y	ɰ	f	g	j	e	ŋ	m	̠	d	ʄ
16	ʔ	u	ː	ɭ	d	g	θ	u	p	ɯ	e	ʃ	̞	ɣ	ʃ
17	q	z	h	ʰ	b	p	v	r	t	p	ɾ	θ	ɾ	o	ː
18	h	j	n	̞	f	b	j	d	x	n	l	l	ɴ	j	l
19	ʒ	ɑ	ˈ	͡	v	ː	ɗ	b	ɛ	ɡ	ʰ	̃	ˈ	u	ɠ
20	b	l	g	͈	g	x	ⁿ	β	θ	ː	p	g	ɡ	̠	r
21	w	ɔ	f	s	m	w	n	ʁ	w	h	ˈ	ɑ	m	ɡ	ɯ
22	ɓ	ə	ð	ɯ	ʒ	z	g	w	b	j	ɦ	ʑ	x	ɯ	d
23	ɾ	ː	j	ɕ	z	u	ɪ	f	d	b	m	β	ʔ	ʁ	p
24	ʃ	ɥ	ɕ	d	n	ɭ	ð	ʷ	ˈ	̚	t	z	ᵝ	v	b
25	ʊ	̞	ᵑ	ɾ	u	d	d	ɯ	̚	̹	ː	ɔ	ɰ	ɴ	e
26	ð	p	ɔ	b	ʃ	ʁ	ɲ	ɲ	l	ʌ	̹	ð	d	ʰ	f
27	z	̠	ˤ	ɡ	w	͡	r	y	̝	ɾ	ʌ	ɲ	p	y	ɣ
28	j	̝	ʁ	ʑ	œ	ʃ	ɠ	̠	ː	ɦ	̠	ɛ	β	̃	ɾ
29	g	͡	ʰ	e	ɲ	r	ᵑ	ː	β	k	ɓ	ɕ	f	ɕ	ɲ
30	ᵐ	ʎ	r	ː	ᵝ	l	ɛ	ŋ	ɡ	ɛ	ɡ	f	ɕ	ø	x
31	ʄ	v	ᵝ	ʝ	ɰ	̃	ʄ	͈	r	ɓ	ʑ	ə	w	ʑ	q
32	θ	ɕ	d	w	ʼ	ð	̞	ʎ	ʁ	̝	b	ʄ	ɛ	͈	h
33	ɗ	w	ʃ	ɲ	̠	y	ɴ	̃	͈	s	ɔ	ʷ	j	ʒ	ˤ
34	ɯ	͈	y	ɦ	ː	ɛ	ɣ	̞	̞	ᵐ	ɴ	ɴ	ʁ	ː	ʔ
35	ᵑ	ɯ	z	h	ø	ɑ	β	ɛ	̹	ʃ	r	x	̚	θ	ʒ
36	̠	g	̠	ʔ	h	ɔ	̚	ɔ	ʌ	̃	ɗ	ˈ	g	ɲ	j
37	e	ʒ	w	ɗ	o	v	b	ɕ	ð	ɔ	g	j	ŋ	͡	ɴ
38	p	ɲ	b	ɛ	ˤ	̞	ʒ	ᵑ	z	l	ʄ	ᵝ	ʃ	g	ʕ
39	o	̚	ɗ	q	ɾ	h	ɔ	θ	ɔ	ʁ	w	ɰ	ʕ	ɑ	v

	form	freq	spell	sound	arabic	bengali	chinese	english	esperanto	finnish	...	russian	spanish	swahili	turkish	language	size	1gram	2gram	3gram	skippy2gram
355	ʔarradˤa	1	0	1	1	0	0	0	0	0	...	0	0	0	0	arabic	8	[ʔ, a, r, r, a, d, ˤ, a]	[ʔa, ar, rr, ra, ad, dˤ, ˤa, ʔ, a, r, r, a, d,...	[ʔar, arr, rra, rad, adˤ, dˤa, ʔa, ar, rr, ra,...	[ʔa, ʔ…r, ʔ…a, ʔ…d, ʔ…ˤ, ar, a…r, a…a, a…d, a…...
70	faʕala	1	0	1	1	0	0	0	0	0	...	0	0	0	0	arabic	6	[f, a, ʕ, a, l, a]	[fa, aʕ, ʕa, al, la, f, a, ʕ, a, l, a]	[faʕ, aʕa, ʕal, ala, fa, aʕ, ʕa, al, la, f, a,...	[fa, f…ʕ, f…a, f…l, aʕ, a…a, a…l, ʕa, ʕ…l, ʕ…a...
53	aˈsi	1.0	0	1	0	0	0	0	0	0	...	0	1	0	0	spanish	4	[a, ˈ, s, i]	[aˈ, ˈs, si, a, ˈ, s, i]	[aˈs, ˈsi, aˈ, ˈs, si, a, ˈ, s, i]	[aˈ, a…s, a…i, ˈs, ˈ…i, si, a, ˈ, s, i]
381	alisimama	1	0	1	0	0	0	0	0	0	...	0	0	1	0	swahili	9	[a, l, i, s, i, m, a, m, a]	[al, li, is, si, im, ma, am, ma, a, l, i, s, i...	[ali, lis, isi, sim, ima, mam, ama, al, li, is...	[al, a…i, a…s, a…m, a…a, li, l…s, l…i, l…m, l…...
434	amɔli	1	0	1	0	0	0	0	0	0	...	0	0	0	0	french	5	[a, m, ɔ, l, i]	[am, mɔ, ɔl, li, a, m, ɔ, l, i]	[amɔ, mɔl, ɔli, am, mɔ, ɔl, li, a, m, ɔ, l, i]	[am, a…ɔ, a…l, a…i, mɔ, m…l, m…i, ɔl, ɔ…i, li,...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
432	mexoɾ	1.0	0	1	0	0	0	0	0	0	...	0	1	0	0	spanish	5	[m, e, x, o, ɾ]	[me, ex, xo, oɾ, m, e, x, o, ɾ]	[mex, exo, xoɾ, me, ex, xo, oɾ, m, e, x, o, ɾ]	[me, m…x, m…o, m…ɾ, ex, e…o, e…ɾ, xo, x…ɾ, oɾ,...
849	diskuɾso	1.0	0	1	0	0	0	0	0	0	...	0	1	0	0	spanish	8	[d, i, s, k, u, ɾ, s, o]	[di, is, sk, ku, uɾ, ɾs, so, d, i, s, k, u, ɾ,...	[dis, isk, sku, kuɾ, uɾs, ɾso, di, is, sk, ku,...	[di, d…s, d…k, d…u, d…ɾ, d…o, is, i…k, i…u, i…...
380	baːrid	1	0	1	1	0	0	0	0	0	...	0	0	0	0	arabic	6	[b, a, ː, r, i, d]	[ba, aː, ːr, ri, id, b, a, ː, r, i, d]	[baː, aːr, ːri, rid, ba, aː, ːr, ri, id, b, a,...	[ba, b…ː, b…r, b…i, b…d, aː, a…r, a…i, a…d, ːr...
665	aːlnasaːʔ	1	0	1	1	0	0	0	0	0	...	0	0	0	0	arabic	9	[a, ː, l, n, a, s, a, ː, ʔ]	[aː, ːl, ln, na, as, sa, aː, ːʔ, a, ː, l, n, a...	[aːl, ːln, lna, nas, asa, saː, aːʔ, aː, ːl, ln...	[aː, a…l, a…n, a…a, a…s, a…ː, a…ʔ, ːl, ː…n, ː…...
562	ɗamu	1	0	1	0	0	0	0	0	0	...	0	0	1	0	swahili	4	[ɗ, a, m, u]	[ɗa, am, mu, ɗ, a, m, u]	[ɗam, amu, ɗa, am, mu, ɗ, a, m, u]	[ɗa, ɗ…m, ɗ…u, am, a…u, mu, ɗ, a, m, u]

	form	freq	spell	sound	arabic	bengali	chinese	english	esperanto	finnish	...	spanish	swahili	turkish	language	size	1gram	2gram	3gram	skippy2gram	skippy3gram
355	ʔarradˤa	1	0	1	1	0	0	0	0	0	...	0	0	0	arabic	8	[ʔ, a, r, r, a, d, ˤ, a]	[ʔa, ar, rr, ra, ad, dˤ, ˤa, ʔ, a, r, r, a, d,...	[ʔar, arr, rra, rad, adˤ, dˤa, ʔa, ar, rr, ra,...	[ʔa, ʔ…r, ʔ…a, ʔ…d, ʔ…ˤ, ar, a…r, a…a, a…d, a…...	[ʔar, ʔa…r, ʔa…a, ʔa…d, ʔa…ˤ, ʔ…rr, ʔ…r…a, ʔ…r...
70	faʕala	1	0	1	1	0	0	0	0	0	...	0	0	0	arabic	6	[f, a, ʕ, a, l, a]	[fa, aʕ, ʕa, al, la, f, a, ʕ, a, l, a]	[faʕ, aʕa, ʕal, ala, fa, aʕ, ʕa, al, la, f, a,...	[fa, f…ʕ, f…a, f…l, aʕ, a…a, a…l, ʕa, ʕ…l, ʕ…a...	[faʕ, fa…a, fa…l, f…ʕa, f…ʕ…l, f…ʕ…a, f…al, f…...
53	aˈsi	1.0	0	1	0	0	0	0	0	0	...	1	0	0	spanish	4	[a, ˈ, s, i]	[aˈ, ˈs, si, a, ˈ, s, i]	[aˈs, ˈsi, aˈ, ˈs, si, a, ˈ, s, i]	[aˈ, a…s, a…i, ˈs, ˈ…i, si, a, ˈ, s, i]	[aˈs, aˈ…i, a…si, ˈsi, aˈ, a…s, a…i, ˈs, ˈ…i, ...
381	alisimama	1	0	1	0	0	0	0	0	0	...	0	1	0	swahili	9	[a, l, i, s, i, m, a, m, a]	[al, li, is, si, im, ma, am, ma, a, l, i, s, i...	[ali, lis, isi, sim, ima, mam, ama, al, li, is...	[al, a…i, a…s, a…m, a…a, li, l…s, l…i, l…m, l…...	[ali, al…s, al…i, al…m, al…a, a…is, a…i…i, a…i...
434	amɔli	1	0	1	0	0	0	0	0	0	...	0	0	0	french	5	[a, m, ɔ, l, i]	[am, mɔ, ɔl, li, a, m, ɔ, l, i]	[amɔ, mɔl, ɔli, am, mɔ, ɔl, li, a, m, ɔ, l, i]	[am, a…ɔ, a…l, a…i, mɔ, m…l, m…i, ɔl, ɔ…i, li,...	[amɔ, am…l, am…i, a…ɔl, a…ɔ…i, a…li, mɔl, mɔ…i...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
432	mexoɾ	1.0	0	1	0	0	0	0	0	0	...	1	0	0	spanish	5	[m, e, x, o, ɾ]	[me, ex, xo, oɾ, m, e, x, o, ɾ]	[mex, exo, xoɾ, me, ex, xo, oɾ, m, e, x, o, ɾ]	[me, m…x, m…o, m…ɾ, ex, e…o, e…ɾ, xo, x…ɾ, oɾ,...	[mex, me…o, me…ɾ, m…xo, m…x…ɾ, m…oɾ, exo, ex…ɾ...
849	diskuɾso	1.0	0	1	0	0	0	0	0	0	...	1	0	0	spanish	8	[d, i, s, k, u, ɾ, s, o]	[di, is, sk, ku, uɾ, ɾs, so, d, i, s, k, u, ɾ,...	[dis, isk, sku, kuɾ, uɾs, ɾso, di, is, sk, ku,...	[di, d…s, d…k, d…u, d…ɾ, d…o, is, i…k, i…u, i…...	[dis, di…k, di…u, di…ɾ, di…s, di…o, d…sk, d…s…...
380	baːrid	1	0	1	1	0	0	0	0	0	...	0	0	0	arabic	6	[b, a, ː, r, i, d]	[ba, aː, ːr, ri, id, b, a, ː, r, i, d]	[baː, aːr, ːri, rid, ba, aː, ːr, ri, id, b, a,...	[ba, b…ː, b…r, b…i, b…d, aː, a…r, a…i, a…d, ːr...	[baː, ba…r, ba…i, ba…d, b…ːr, b…ː…i, b…ː…d, b…...
665	aːlnasaːʔ	1	0	1	1	0	0	0	0	0	...	0	0	0	arabic	9	[a, ː, l, n, a, s, a, ː, ʔ]	[aː, ːl, ln, na, as, sa, aː, ːʔ, a, ː, l, n, a...	[aːl, ːln, lna, nas, asa, saː, aːʔ, aː, ːl, ln...	[aː, a…l, a…n, a…a, a…s, a…ː, a…ʔ, ːl, ː…n, ː…...	[aːl, aː…n, aː…a, aː…s, aː…ː, aː…ʔ, a…ln, a…l…...
562	ɗamu	1	0	1	0	0	0	0	0	0	...	0	1	0	swahili	4	[ɗ, a, m, u]	[ɗa, am, mu, ɗ, a, m, u]	[ɗam, amu, ɗa, am, mu, ɗ, a, m, u]	[ɗa, ɗ…m, ɗ…u, am, a…u, mu, ɗ, a, m, u]	[ɗam, ɗa…u, ɗ…mu, amu, ɗa, ɗ…m, ɗ…u, am, a…u, ...

	topic 1	topic 2	topic 3	topic 4	topic 5	topic 6	topic 7	topic 8	topic 9	topic 10	topic 11	topic 12	topic 13	topic 14	topic 15
0	a	t	i	ʌ	ʁ	ɯ	e	a	a	i	u	o	a	i	a
1	ː	a	a	̹	a	o	i	e	̠	ɕ	s	e	s	a	k
2	i	e	k	k	ɔ	i	p	ɾ	k	t	j	ɾ	o	n	o
3	m	i	s	a	̃	m	o	j	n	ʰ	i	ɯ	t	k	u
4	l	n	o	j	s	s	a	m	e	͡	o	k	b	l	i
5	d	s	ɾ	t	t	ɾ	l	o	s	̞	n	t	l	z	t
6	r	ʁ	u	̚	ɛ	e	u	t	ɯ	d	θ	d	i	t	m
7	u	m	x	i	ə	t	ɾ	ð	ɭ	o	a	ː	ː	m	ɗ
8	t	ɛ	m	n	i	ɕ	ɓ	p	m	ʑ	̝	i	u	p	g
9	s	k	e	o	l	n	w	s	u	a	ɛ	b	e	ɾ	n
10	n	d	p	̠	k	k	s	k	o	̠	d	a	ɯ	w	ᵑ
11	ħ	o	ɯ	l	e	β	k	n	i	ŋ	k	n	k	e	w
12	k	ɾ	t	ŋ	j	ɣ	z	ˈ	ʰ	ɴ	h	s	ʰ	s	s
13	ʕ	b	ç	p	ɑ	ɴ	t	l	ɾ	u	ɯ	p	ˤ	ɔ	z
14	f	ʃ	ɴ	u	p	ʎ	ᵐ	i	ŋ	m	f	ɸ	n	ʃ	ⁿ
15	ˤ	̃	l	m	y	ɰ	f	g	j	e	ŋ	m	̠	d	ʄ
16	ʔ	u	ː	ɭ	d	g	θ	u	p	ɯ	e	ʃ	̞	ɣ	ʃ
17	q	z	h	ʰ	b	p	v	r	t	p	ɾ	θ	ɾ	o	ː
18	h	j	n	̞	f	b	j	d	x	n	l	l	ɴ	j	l
19	ʒ	ɑ	ˈ	͡	v	ː	ɗ	b	ɛ	ɡ	ʰ	̃	ˈ	u	ɠ
20	b	l	g	͈	g	x	ⁿ	β	θ	ː	p	g	ɡ	̠	r
21	w	ɔ	f	s	m	w	n	ʁ	w	h	ˈ	ɑ	m	ɡ	ɯ
22	ɓ	ə	ð	ɯ	ʒ	z	g	w	b	j	ɦ	ʑ	x	ɯ	d
23	ɾ	ː	j	ɕ	z	u	ɪ	f	d	b	m	β	ʔ	ʁ	p
24	ʃ	ɥ	ɕ	d	n	ɭ	ð	ʷ	ˈ	̚	t	z	ᵝ	v	b
25	ʊ	̞	ᵑ	ɾ	u	d	d	ɯ	̚	̹	ː	ɔ	ɰ	ɴ	e
26	ð	p	ɔ	b	ʃ	ʁ	ɲ	ɲ	l	ʌ	̹	ð	d	ʰ	f
27	z	̠	ˤ	ɡ	w	͡	r	y	̝	ɾ	ʌ	ɲ	p	y	ɣ
28	j	̝	ʁ	ʑ	œ	ʃ	ɠ	̠	ː	ɦ	̠	ɛ	β	̃	ɾ
29	g	͡	ʰ	e	ɲ	r	ᵑ	ː	β	k	ɓ	ɕ	f	ɕ	ɲ
30	ᵐ	ʎ	r	ː	ᵝ	l	ɛ	ŋ	ɡ	ɛ	ɡ	f	ɕ	ø	x
31	ʄ	v	ᵝ	ʝ	ɰ	̃	ʄ	͈	r	ɓ	ʑ	ə	w	ʑ	q
32	θ	ɕ	d	w	ʼ	ð	̞	ʎ	ʁ	̝	b	ʄ	ɛ	͈	h
33	ɗ	w	ʃ	ɲ	̠	y	ɴ	̃	͈	s	ɔ	ʷ	j	ʒ	ˤ
34	ɯ	͈	y	ɦ	ː	ɛ	ɣ	̞	̞	ᵐ	ɴ	ɴ	ʁ	ː	ʔ
35	ᵑ	ɯ	z	h	ø	ɑ	β	ɛ	̹	ʃ	r	x	̚	θ	ʒ
36	̠	g	̠	ʔ	h	ɔ	̚	ɔ	ʌ	̃	ɗ	ˈ	g	ɲ	j
37	e	ʒ	w	ɗ	o	v	b	ɕ	ð	ɔ	g	j	ŋ	͡	ɴ
38	p	ɲ	b	ɛ	ˤ	̞	ʒ	ᵑ	z	l	ʄ	ᵝ	ʃ	g	ʕ
39	o	̚	ɗ	q	ɾ	h	ɔ	θ	ɔ	ʁ	w	ɰ	ʕ	ɑ	v

解析¶

	topic 1	topic 2	topic 3	topic 4	topic 5	topic 6	topic 7	topic 8	topic 9	topic 10	topic 11	topic 12	topic 13	topic 14	topic 15
0	a	t	i	ʌ	ʁ	ɯ	e	a	a	i	u	o	a	i	a
1	ː	a	a	̹	a	o	i	e	̠	ɕ	s	e	s	a	k
2	i	e	k	k	ɔ	i	p	ɾ	k	t	j	ɾ	o	n	o
3	m	i	s	a	̃	m	o	j	n	ʰ	i	ɯ	t	k	u
4	l	n	o	j	s	s	a	m	e	͡	o	k	b	l	i
5	d	s	ɾ	t	t	ɾ	l	o	s	̞	n	t	l	z	t
6	r	ʁ	u	̚	ɛ	e	u	t	ɯ	d	θ	d	i	t	m
7	u	m	x	i	ə	t	ɾ	ð	ɭ	o	a	ː	ː	m	ɗ
8	t	ɛ	m	n	i	ɕ	ɓ	p	m	ʑ	̝	i	u	p	g
9	s	k	e	o	l	n	w	s	u	a	ɛ	b	e	ɾ	n
10	n	d	p	̠	k	k	s	k	o	̠	d	a	ɯ	w	ᵑ
11	ħ	o	ɯ	l	e	β	k	n	i	ŋ	k	n	k	e	w
12	k	ɾ	t	ŋ	j	ɣ	z	ˈ	ʰ	ɴ	h	s	ʰ	s	s
13	ʕ	b	ç	p	ɑ	ɴ	t	l	ɾ	u	ɯ	p	ˤ	ɔ	z
14	f	ʃ	ɴ	u	p	ʎ	ᵐ	i	ŋ	m	f	ɸ	n	ʃ	ⁿ
15	ˤ	̃	l	m	y	ɰ	f	g	j	e	ŋ	m	̠	d	ʄ
16	ʔ	u	ː	ɭ	d	g	θ	u	p	ɯ	e	ʃ	̞	ɣ	ʃ
17	q	z	h	ʰ	b	p	v	r	t	p	ɾ	θ	ɾ	o	ː
18	h	j	n	̞	f	b	j	d	x	n	l	l	ɴ	j	l
19	ʒ	ɑ	ˈ	͡	v	ː	ɗ	b	ɛ	ɡ	ʰ	̃	ˈ	u	ɠ
20	b	l	g	͈	g	x	ⁿ	β	θ	ː	p	g	ɡ	̠	r
21	w	ɔ	f	s	m	w	n	ʁ	w	h	ˈ	ɑ	m	ɡ	ɯ
22	ɓ	ə	ð	ɯ	ʒ	z	g	w	b	j	ɦ	ʑ	x	ɯ	d
23	ɾ	ː	j	ɕ	z	u	ɪ	f	d	b	m	β	ʔ	ʁ	p
24	ʃ	ɥ	ɕ	d	n	ɭ	ð	ʷ	ˈ	̚	t	z	ᵝ	v	b
25	ʊ	̞	ᵑ	ɾ	u	d	d	ɯ	̚	̹	ː	ɔ	ɰ	ɴ	e
26	ð	p	ɔ	b	ʃ	ʁ	ɲ	ɲ	l	ʌ	̹	ð	d	ʰ	f
27	z	̠	ˤ	ɡ	w	͡	r	y	̝	ɾ	ʌ	ɲ	p	y	ɣ
28	j	̝	ʁ	ʑ	œ	ʃ	ɠ	̠	ː	ɦ	̠	ɛ	β	̃	ɾ
29	g	͡	ʰ	e	ɲ	r	ᵑ	ː	β	k	ɓ	ɕ	f	ɕ	ɲ
30	ᵐ	ʎ	r	ː	ᵝ	l	ɛ	ŋ	ɡ	ɛ	ɡ	f	ɕ	ø	x
31	ʄ	v	ᵝ	ʝ	ɰ	̃	ʄ	͈	r	ɓ	ʑ	ə	w	ʑ	q
32	θ	ɕ	d	w	ʼ	ð	̞	ʎ	ʁ	̝	b	ʄ	ɛ	͈	h
33	ɗ	w	ʃ	ɲ	̠	y	ɴ	̃	͈	s	ɔ	ʷ	j	ʒ	ˤ
34	ɯ	͈	y	ɦ	ː	ɛ	ɣ	̞	̞	ᵐ	ɴ	ɴ	ʁ	ː	ʔ
35	ᵑ	ɯ	z	h	ø	ɑ	β	ɛ	̹	ʃ	r	x	̚	θ	ʒ
36	̠	g	̠	ʔ	h	ɔ	̚	ɔ	ʌ	̃	ɗ	ˈ	g	ɲ	j
37	e	ʒ	w	ɗ	o	v	b	ɕ	ð	ɔ	g	j	ŋ	͡	ɴ
38	p	ɲ	b	ɛ	ˤ	̞	ʒ	ᵑ	z	l	ʄ	ᵝ	ʃ	g	ʕ
39	o	̚	ɗ	q	ɾ	h	ɔ	θ	ɔ	ʁ	w	ɰ	ʕ	ɑ	v